# Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from transformers import AutoModelForCausalLM

from megatron import get_args
from megatron.initialize import initialize_megatron

from megatron_patch.data.finetune_dataset import LLamaDataset
from megatron_patch.finetune_utils import finetune
from megatron_patch.tokenizer import build_tokenizer
from megatron_patch.tokenizer import get_tokenizer
from megatron_patch.arguments import get_patch_args

def model_provider(pre_process=True, post_process=True):
    args = get_args()
    tokenizer = get_tokenizer()
    model = AutoModelForCausalLM.from_pretrained(args.load,
                                                 trust_remote_code=True)
    model.resize_token_embeddings(len(tokenizer))
    return model


def train_valid_datasets_provider():
    """Build train and validation dataset."""
    args = get_args()
    tokenizer = build_tokenizer(args)
    train_dataset = LLamaDataset(args.train_data, tokenizer,
                                 args.max_padding_length)
    valid_dataset = LLamaDataset(args.valid_data, tokenizer,
                                 args.max_padding_length)
    return train_dataset, valid_dataset


def forward_step(data_iterator, model):
    tokenizer = get_tokenizer()

    try:
        data_iterator = next(data_iterator)
    except BaseException:
        data_iterator = data_iterator

    input_ids = data_iterator['input_ids'].cuda()
    labels = data_iterator['labels'].cuda()
    attention_mask = input_ids.ne(tokenizer.pad_token_id)
    output_tensor = model(input_ids=input_ids,
                          labels=labels,
                          attention_mask=attention_mask)
    return output_tensor.loss


if __name__ == '__main__':
    initialize_megatron(extra_args_provider=get_patch_args)

    finetune(train_valid_datasets_provider=train_valid_datasets_provider,
             model_provider=model_provider,
             forward_step=forward_step)
